R Notebook

Importing the libraries

library(tidyverse)
library(plotly)

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

Importing the data-set

# National Parks in California
ca = read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/ca.csv") 

Rows: 789 Columns: 7
── Column specification ───────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): region, state, code, park_name, type
dbl (2): visitors, year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(ca)

# Acadia National Park
acadia <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/acadia.csv")

Rows: 98 Columns: 7
── Column specification ───────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): region, state, code, park_name, type
dbl (2): visitors, year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(acadia)

# Southeast US National Parks
se <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/se.csv")

Rows: 453 Columns: 7
── Column specification ───────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): region, state, code, park_name, type
dbl (2): visitors, year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(se)

# 2016 Visitation for all Pacific West National Parks
visit_16 <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/visit_16.csv")

Rows: 17 Columns: 7
── Column specification ───────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): region, state, code, park_name, type
dbl (2): visitors, year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(visit_16)

# All Nationally designated sites in Massachusetts
mass <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/mass.csv")

Rows: 13 Columns: 7
── Column specification ───────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): region, state, code, park_name, type
dbl (2): visitors, year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(mass)

Plot-2 –> ggplot

Scatter plot

scatter_plot = plot_ly(data=ca, x=~year, y=~visitors,color = ~park_name, type='scatter',mode='markers') %>%
                       layout(
                          title= list(text = "<b>Body weight vs Brain weight"),
                          legend = list(title = list(text ='<b>Animals')), 
                          xaxis  = list(title = list(text ='<b>Brain Weight')),
                          yaxis  = list(title = list(text ='<b>Body Weight')))
scatter_plot

Warning in RColorBrewer::brewer.pal(N, "Set2") :
  n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

Warning in RColorBrewer::brewer.pal(N, "Set2") :
  n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

Warning in RColorBrewer::brewer.pal(N, "Set2") :
  n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

Warning in RColorBrewer::brewer.pal(N, "Set2") :
  n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

Barchart

r_group_barchart = data.table::melt(ca, id.vars='park_name') %>%
plot_ly(x = ~park_name, y = ~value, type = 'bar', name = ~variable, color = ~variable) %>%
      layout(
          title= list(text = "<b>Total Distribution based on Vore"),
          legend = list(title = list(text= '<b>Aniamal Feature')),
          xaxis  = list(title = list(text ='<b>Vores')),
          yaxis = list(title='Count', text='<b>Count'), barmode = 'group')

Warning in data.table::melt(ca, id.vars = "park_name") :
  The melt generic in data.table has been passed a spec_tbl_df and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(ca). In the next version, this warning will become an error.

r_group_barchart

pie-chart

df_order = data.frame(table(ca$park_name))
df_order

pie_chart = plot_ly(type='pie', labels=df_order$Var1, values=df_order$Freq, 
                    textinfo='label+percent',insidetextorientation='radial') %>%
                    layout(
                          title= list(text = "<b>Order Distributions"),
                          legend = list(title = list(text= '<b>Order')))

pie_chart

histogram_plot = plot_ly(data = ca, x = ~(log(visitors)), name=~code,type="histogram") %>%
                  layout(
                          title= list(text = "<b>Total sleep time of Animals based on Vore"),
                          legend = list(title = list(text= '<b>Vore')),
                          xaxis  = list(title = list(text ='<b>Visitors')),
                          yaxis  = list(title = list(text ='<b>Count')))
histogram_plot

Donut Chart / Open Pie-Chart

df_vore = data.frame(table(ca$code))
df_vore

donut_chart = plot_ly(labels=df_vore$Var1, values=df_vore$Freq, 
                    textinfo='label+percent') %>%
                    add_pie(hole = 0.6) %>%
                    layout(
                          title= list(text = "<b>Order Distributions"),
                          legend = list(title = list(text= '<b>Order')))

donut_chart